Event-Triggered MPC With Linear Inter-Event Control for AV Path Tracking

Abstract

Model predictive control (MPC) is widely used for autonomous vehicle path tracking due to its ability to handle system constraints and optimize performance over a prediction horizon. However, frequent online optimization imposes high computational demands, making the real-time implementation of MPC challenging. Event-triggered MPC aims to solve this issue by updating control actions only when a predefined condition is met, but it executes precomputed control sequences in an open-loop fashion between events, potentially allowing errors to accumulate. This letter proposes an event-triggered MPC framework integrated with a linear inter-event control mechanism to address this limitation. The proposed inter-event controller applies a least-squares-based linear model to generate control inputs in real-time during inter-event periods, enabling continuous feedback corrections. Experimental results on a Quanser QCar2 platform demonstrate that the proposed approach improves tracking accuracy by 10% while significantly reducing the number of MPC optimizations compared to standard event-triggered MPC, offering an efficient solution for real-time path tracking problem.